class_docstr: reading dataset from CoNLL dataset, extracted by https://github.com/dalab/deep-ed/
class_docstr_tok: ["reading","dataset","from","CoNLL","dataset",",","extracted","by","https",":","\/\/github.com\/dalab\/deep-ed\/"]
code: def __init__(self, path, person_path, conll_path, added_params):
      if added_params["generate_ments_and_cands"]:
          added_params["generate_cands"] = False
  
      if (
          added_params["generate_cands"]
          or added_params["generate_ments_and_cands"]
      ):
          added_params["cand_generator"] = get_candidate_generator(
              added_params
          )
  
      print(added_params)
  
      print("load csv")
      self.train = read_csv_file(path + "/aida_train.csv", added_params)
      self.testA = read_csv_file(path + "/aida_testA.csv", added_params)
      self.testB = read_csv_file(path + "/aida_testB.csv", added_params)
      self.ace2004 = read_csv_file(
          path + "/wned-ace2004.csv", added_params
      )
      self.aquaint = read_csv_file(
          path + "/wned-aquaint.csv", added_params
      )
      self.clueweb = read_csv_file(
          path + "/wned-clueweb.csv", added_params
      )
      self.msnbc = read_csv_file(path + "/wned-msnbc.csv", added_params)
      self.wikipedia = read_csv_file(
          path + "/wned-wikipedia.csv", added_params
      )
      self.wikipedia.pop("Jiří_Třanovský Jiří_Třanovský", None)
  
      print("process coref")
      person_names = load_person_names(person_path)
      with_coref(self.train, person_names)
      with_coref(self.testA, person_names)
      with_coref(self.testB, person_names)
      with_coref(self.ace2004, person_names)
      with_coref(self.aquaint, person_names)
      with_coref(self.clueweb, person_names)
      with_coref(self.msnbc, person_names)
      with_coref(self.wikipedia, person_names)
  
      print("load conll")
      read_conll_file(self.train, conll_path + "/AIDA/aida_train.txt")
      read_conll_file(
          self.testA,
          conll_path + "/AIDA/testa_testb_aggregate_original",
      )
      read_conll_file(
          self.testB,
          conll_path + "/AIDA/testa_testb_aggregate_original",
      )
      read_conll_file(
          self.ace2004,
          conll_path + "/wned-datasets/ace2004/ace2004.conll",
      )
      read_conll_file(
          self.aquaint,
          conll_path + "/wned-datasets/aquaint/aquaint.conll",
      )
      read_conll_file(
          self.msnbc, conll_path + "/wned-datasets/msnbc/msnbc.conll"
      )
      read_conll_file(
          self.clueweb,
          conll_path + "/wned-datasets/clueweb/clueweb.conll",
      )
      read_conll_file(
          self.wikipedia,
          conll_path + "/wned-datasets/wikipedia/wikipedia.conll",
      )
  
      if added_params["generate_cands"]:
          print(
              "Number of candidates not present in p_e_m originally, but present when lowercased",
              len(added_params["cand_generator"].lower_org),
          )
          print(
              "Number of candidates not present in p_e_m originally, but present in p_e_m_lower when lowercased ",
              len(added_params["cand_generator"].lower_lower),
          )
  
file_path: BLINK/blink/candidate_retrieval/dataset.py
imports: import re
  import pickle
  import os
  import time
  import numpy
label_desc: Use image node_id 1 to create a new CoNLLDataset object  with example: obj = CoNLLDataset(path, person_path, conll_path, added_params)